Parameters Identification of Solar PV Using Hybrid Chaotic Northern Goshawk and Pattern Search

Sustainability, Journal Year: 2023, Volume and Issue: 15(6), P. 5027 - 5027

Published: March 12, 2023

This article proposes an effective evolutionary hybrid optimization method for identifying unknown parameters in photovoltaic (PV) models based on the northern goshawk algorithm (NGO) and pattern search (PS). The chaotic sequence is used to improve exploration capability of NGO technique while evading premature convergence. suggested algorithm, goshawk, (CNGPS), takes advantage algorithm’s global as well method’s powerful local capability. effectiveness recommended CNGPS verified through use mathematical test functions, its results are contrasted with those a conventional other methods. then extract PV parameters, parameter identification defined objective function be minimized difference between estimated experimental data. usefulness extraction evaluated using three distinct models: SDM, DDM, TDM. numerical investigates illustrate that new may produce better optimum solutions outperform previous approaches literature. simulation display novel achieves lowest root mean square error obtains optima than existing methods various solar cells.

Language: Английский

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Hybrid Tiki Taka and Mean Differential Evolution based Weibull distribution: A comprehensive approach for solar PV modules parameter extraction with Newton-Raphson optimization

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 314, P. 118705 - 118705

Published: June 24, 2024

Language: Английский

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Parameters identification of photovoltaic models using Lambert W-function and Newton-Raphson method collaborated with AI-based optimization techniques: A comparative study

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 255, P. 124777 - 124777

Published: July 14, 2024

Accurately estimating the unknown parameters of photovoltaic (PV) models based on measured voltage-current data is a challenging optimization problem due to its high nonlinearity and multimodality. An accurate solution this essential for efficiently simulating, controlling, evaluating PV systems. There are three different models, including single-diode model, double-diode triple-diode with five, seven, nine parameters, respectively, proposed represent electrical characteristics systems varying levels complexity accuracy. In literature, several deterministic metaheuristic algorithms have been used accurately solve hard problem. However, problem, methods could not achieve solutions. On other side, algorithms, also known as gradient-free methods, somewhat good solutions but they still need further improvements strengthen their performance against stuck-in local optima slow convergence speed problems. Over last two years, recent better improve avoid tackle continuous majority those has investigated. Therefore, in paper, nineteen recently published such Mantis search algorithm (MSA), spider wasp optimizer (SWO), light spectrum (LSO), growth (GO), walrus (WAOA), hippopotamus (HOA), black-winged kite (BKA), quadratic interpolation (QIO), sinh cosh (SCHA), exponential distribution (EDO), optical microscope (OMA), secretary bird (SBOA), Parrot Optimizer (PO), Newton-Raphson-based (NRBO), crested porcupine (CPO), differentiated creative (DCS), propagation (PSA), one-to-one (OOBO), triangulation topology aggregation (TTAO), studied clarify effectiveness models. addition, collaborate functions, namely Lambert W-Function Newton-Raphson Method, aid solving I-V curve equations more accurately, thereby improving Those assessed using four well-known solar cells modules compared each metrics, best fitness, average worst standard deviation (SD), Friedman mean rank, speed; multiple-comparison test compare difference between ranks. Results comparison show that SWO efficient effective SDM, DDM, TDM over modules, Method equations. study reports perform poorly when applied

Language: Английский

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Efficient estimation of PV parameters for existing datasets by using an intelligent algorithm

Optik, Journal Year: 2023, Volume and Issue: 295, P. 171467 - 171467

Published: Oct. 18, 2023

Language: Английский

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Parameter Extraction of Single, Double, and Triple‐Diode Photovoltaic Models Using the Weighted Leader Search Algorithm

Global Challenges, Journal Year: 2024, Volume and Issue: 8(5)

Published: April 18, 2024

Abstract This study presents the parameter extraction of single, double, and triple‐diode photovoltaic (PV) models using weighted leader search algorithm (WLS). The primary objective is to develop that accurately reflect characteristics PV devices so technical economic benefits are maximized under all constraints. For this purpose, 24 models, 6 for two different cells, 18 six modules, whose experimental data publicly available, developed successfully. second research selection most suitable problem. It a significant challenge since evaluation process requires advanced statistical tools techniques determine reliable selection. Therefore, seven brand‐new algorithms, including WLS, spider wasp optimizer, shrimp goby association search, reversible elementary cellular automata, fennec fox optimization, Kepler rime optimization tested. WLS has yielded smallest minimum, average, RMSE, standard deviation among those. Its superiority also verified by Friedman Wilcoxon signed‐rank test based on 144 pairwise comparisons. In conclusion, it demonstrated superior in developing accurate models.

Language: Английский

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A comparative study of the performance of ten metaheuristic algorithms for parameter estimation of solar photovoltaic models

PeerJ Computer Science, Journal Year: 2025, Volume and Issue: 11, P. e2646 - e2646

Published: Jan. 27, 2025

This study conducts a comparative analysis of the performance ten novel and well-performing metaheuristic algorithms for parameter estimation solar photovoltaic models. optimization problem involves accurately identifying parameters that reflect complex nonlinear behaviours cells affected by changing environmental conditions material inconsistencies. is challenging due to computational complexity risk errors, which can hinder reliable predictions. The evaluated include Crayfish Optimization Algorithm, Golf Coati Crested Porcupine Optimizer, Growth Artificial Protozoa Secretary Bird Mother Election Optimizer Technical Vocational Education Training-Based Optimizer. These are applied solve four well-established models: single-diode model, double-diode triple-diode different module focuses on key metrics such as execution time, number function evaluations, solution optimality. results reveal significant differences in efficiency accuracy algorithms, with some demonstrating superior specific Friedman test was utilized rank various revealing top performer across all considered optimizer achieved root mean square error 9.8602187789E-04 9.8248487610E-04 both models 1.2307306856E-02 model. consistent success indicates strong contender future enhancements aimed at further boosting its effectiveness. Its current suggests potential improvement, making it promising focus ongoing development efforts. findings contribute understanding applicability renewable energy systems, providing valuable insights optimizing

Language: Английский

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Chaos-enhanced metaheuristics: classification, comparison, and convergence analysis

Complex & Intelligent Systems, Journal Year: 2025, Volume and Issue: 11(3)

Published: Feb. 19, 2025

Language: Английский

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Hybrid Brown-Bear and Hippopotamus Algorithms with Fractional Order Chaos Maps for Precise Solar PV Model Parameter Estimation

Processes, Journal Year: 2024, Volume and Issue: 12(12), P. 2718 - 2718

Published: Dec. 2, 2024

The rise in photovoltaic (PV) energy utilization has led to increased research on its functioning, as accurate modeling is crucial for system simulations. However, capturing nonlinear current–voltage traits challenging due limited data from cells’ datasheets. This paper presents a novel enhanced version of the Brown-Bear Optimization Algorithm (EBOA) determining ideal parameters circuit model. presented EBOA incorporates several modifications aimed at improving searching capabilities. It combines Fractional-order Chaos maps (FC maps), which support BOA settings be adjusted an adaptive manner. Additionally, it integrates key mechanisms Hippopotamus (HO) strengthen algorithm’s exploitation potential by leveraging surrounding knowledge more effective position updates while also balance between global and local search processes. was subjected extensive mathematical validation through application benchmark functions rigorously assess performance. Also, PV parameter estimation achieved combining with Newton–Raphson approach. Numerous module cell varieties, including RTC France, STP6-120/36, Photowatt-PWP201, were assessed using double-diode single-diode models. higher performance shown statistical comparison many well-known metaheuristic techniques. To illustrate this, root mean-squared error values our scheme (SDM, DDM) PWP201 are follows: (8.183847 × 10−4, 7.478488 10−4), (1.430320 10−2, 1.427010 10−2), (2.220075 10−3, 2.061273 10−3), respectively. experimental results show that works better than alternative techniques terms accuracy, consistency, convergence.

Language: Английский

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Accurate extraction of electrical parameters in three-diode photovoltaic systems through the enhanced mother tree methodology: A novel approach for parameter estimation

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(3), P. e0318575 - e0318575

Published: March 4, 2025

Accurately simulating photovoltaic (PV) modules requires precise parameter extraction, a complex task due to the nonlinear nature of these systems. This study introduces Mother Tree Optimization with Climate Change (MTO-CL) algorithm address this challenge by enhancing estimation for solar PV three-diode model. MTO-CL improves optimization performance incorporating climate change-inspired adaptations, which affect two key phases: elimination (refreshing 20% suboptimal solutions) and distortion (slight adjustments 80% remaining solutions). balance between exploration exploitation allows dynamically effectively identify optimal parameters. Compared seven alternative methods, shows superior in various modules, including ST40 SM55, across different irradiances temperatures. It achieves exceptionally low Root Mean Square Error (RMSE) values from 0.0025A 0.0165A Squared (MSE) 6.2 × 10^-6 2.7 10^-4, while also significantly minimizing power errors, ranging 22.86 mW 239.40 mW. These results demonstrate MTO-CL's effectiveness improving accuracy reliability system modeling, offering robust tool enhanced energy applications.

Language: Английский

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Harnessing hybrid intelligence: Four vector metaheuristic and differential evolution for optimized photovoltaic parameter extraction

Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 123, P. 110276 - 110276

Published: March 19, 2025

Language: Английский

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